Many sensor network applications require that each node's sensor
stream be annotated with its physical location in some common
coordinate system. Manual measurement and configuration methods for
obtaining location don't scale and are error-prone, and equipping
sensors with GPS is often expensive and does not work in indoor and
urban deployments. Sensor networks can therefore benefit from a
self-configuring method where nodes cooperate with each other,
estimate local distances to their neighbors, and converge to a
consistent coordinate assignment.
This paper describes a fully decentralized algorithm called {\em AFL}
(Anchor-Free Localization) where nodes start from a random initial
coordinate assignment and converge to a consistent solution using only
local node interactions. The key idea in AFL is {\em fold-freedom},
where nodes first configure into a topology that resembles a scaled
and unfolded version of the true configuration, and then run a
force-based relaxation procedure. We show using extensive simulations
under a variety of network sizes, node densities, and distance
estimation errors that our algorithm is superior to previously
proposed methods that incrementally compute the coordinates of nodes
in the network, in terms of its ability to compute correct coordinates
under a wider variety of conditions and its robustness to measurement
errors.